1. Field of the Invention
The present invention is directed to a method and computer program product for identifying patients/members with a high risk of future behavioral health-related hospital admission and subsequently managing those individuals identified as a high risk to reduce the probability of hospital admission.
2. Description of the Related Art
One of many major challenges facing risk-bearing payer organizations in today's managed healthcare environment is to find a way to accurately prospectively identify its members that are considered high risk for utilization of the healthcare system. A second challenge is intervening after identification to maximize the health of the high-risk members. With the appropriate predictive methodology, the payer organization may identify those members considered high risk, e.g., to be at risk of hospital admission and then proactively intervene to assist the identified members modify the risk factors that place them at high risk.
The need for such a method is especially acute in the context of behavioral health issues. Approximately 5% of patients with a recorded diagnosis of a behavioral health condition, e.g., depression or chemical dependency, are hospitalized for a behavioral health-related condition within twelve months of the initial diagnosis. These hospitalized patients incur approximately 50% of the overall total costs for behavioral health professional and facility services. Thus, it is very important that the 5% of patients most at risk of hospitalization due to a behavioral health-related condition are identified and the associated behavioral health condition managed through intervention where possible. Successful intervention will allow these patients to lead more healthy lives and save costs associated with hospitalization while freeing up behavioral health professional and facility resources.
Health plan and provider organizations currently use a variety of methods to screen or evaluate patients for inclusion in health management or case management programs. Some existing methods use claims data to target persons with high prior-cost levels, e.g., Diagnostic Cost Groups, or those with a certain medical condition, e.g., diabetes. Perhaps the most common risk adjustment mechanism in the insurance industry comprises age and gender adjustment. Still others apply survey-based assessment tools. One survey-based approach is described in US patent application publication number 2003/019522, to Meek, et al. Meek discloses a method of developing a risk level for the individual patient utilization of health care services by first obtaining subjective information from the individual patient about his or her perceived health. Meek then generates a risk level for that patient.
Meek's reliance upon subjective data obtained from patients concerning their individual perceived health requires improvement. Such subjective data is simply not as reliable as such data is when combined with additional data derived from a variety of data source(s).
Finally, Johns Hopkins has developed an “Adjusted Clinical Group” (ACG) based risk adjustment methodology. The Johns Hopkins method uses “Adjusted Clinical Groups” (ACGs), which are a series of mutually exclusive, health-status categories that are defined by morbidity, age and gender. They are based on the premise that the level of resources necessary for delivering appropriate health care to a population is correlated to the illness burden of that population. Thus, ACG's are employed in the Johns Hopkins method to predict a population's past or future health care utilization and costs. Essentially, the ACG method leverages the fact that over time, patient/members develop a variety of conditions. Based on the pattern of these conditions, the ACG method assigns each individual to a single group or ACG, thus permitting the effects of a clustering of conditions to be captured in estimates of resource use.
In practice, the Johns Hopkins method assigns all ICD-9-CM codes to one of 32 adjusted diagnosis groups (“ADG”). Diseases may then be placed in an ADG based on the following clinical parameters: Duration; Severity; Diagnostic Certainty; Etiology; and Specialty Care. Thus, all diseases must be classified using such clinical parameters and categorized into the 32 existing ADG's. Ultimately, an algorithm is applied that places patient/members into one of 93 discrete ACG categories. An individual patient/member will be assigned to an ACG based upon his/her particular combination of ADG's as well as his/her age and gender. The net result is that individuals with a certain ACG have experienced a similar morbidity pattern and consumed similar levels of health care resources over the course of a given period of time.
Several problems exist with the Johns Hopkins approach. One of the primary difficulties with ACG's involve a practice referred to commonly within the industry as “upcoding.” Upcoding occurs when providers use diagnoses that result in their patients appearing to have more complicated illnesses than is really the case in order to benefit from additional resources or improve their ratings on case mix, i.e., ACG, adjusted measures of performance. Moreover, because ACGs are mutually exclusive, health status categories defined by morbidity, age and gender, patient/members must fit within a single ACG, thus comparatively less ill (or more ill) individual patients may not be well represented by the “average” illness burden across the entire ACG. Specifically, the Johns Hopkins ACG approach may mask the predictive effects of certain individual patient variables by categorizing patients first into ADG's, then into ACG's, two generalized diagnostic categorization tools for actuarial analysis. The Johns Hopkins ACG method is tuned to predict costs for actuarial purposes, it is not tuned to predict cases where interventions could reduce costs & improve quality.
In general, each existing approach to identification of patients for inclusion in a health management program is filled with problems, including inter alia, error and failure to utilize a predictive model to identify patients for prospective intervention, while still others are inadequate to apply across a wide range of patient morbidities, co-morbidities and other patient-specific variables. Finally, no approach deals specifically with behavioral health predictive methodology.
The invention described herein is a solution to many of the aforementioned problems with current approaches to high-risk patent identification.